To summarize, we make the following contributions:
first,
we introduce a simple architecture for video object segmentation by exploiting motions,
using only optical flow as input.
Second,
we propose a self-supervised proxy task that is used to train the architecture without any manual supervision.
Third,
we conduct thorough ablation studies on the components that are key to the success of our architecture,
such as a consistency loss on optical flow computed from various frame gaps.
Fourth,
we evaluate the proposed architecture on public benchmarks
(DAVIS2016 [1]}, SegTrackv2 [2]}, and FBMS59 [3]}),
outperforming previous state-of-the-art self-supervised models,
with comparable performance to the supervised approaches.
Moreover,
we also evaluate on a camouflage dataset (MoCA [4]}),
demonstrating a significant performance improvement over the other self- and supervised approaches,
highlighting the importance of motion cues,
and the potential bias towards visual appearance in existing video segmentation models.
